Neural Networks
Statistics and neural networks: advances at the interface
Statistics and neural networks: advances at the interface
Neural network credit scoring models
Computers and Operations Research - Neural networks in business
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Neural and Wavelet Network Models for Financial Distress Classification
Data Mining and Knowledge Discovery
Variable selection for financial distress classification using a genetic algorithm
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Soft computing system for bank performance prediction
Applied Soft Computing
Predicting bankruptcies using rough set approach: the case of Turkish banks
MATH'08 Proceedings of the American Conference on Applied Mathematics
Expert Systems with Applications: An International Journal
A Genetic Programming Approach for Bankruptcy Prediction Using a Highly Unbalanced Database
Proceedings of the 2007 EvoWorkshops 2007 on EvoCoMnet, EvoFIN, EvoIASP,EvoINTERACTION, EvoMUSART, EvoSTOC and EvoTransLog: Applications of Evolutionary Computing
Financial distress prediction by a radial basis function network with logit analysis learning
Computers & Mathematics with Applications
Bankruptcy prediction with neural logic networks by means of grammar-guided genetic programming
Expert Systems with Applications: An International Journal
Bankruptcy prediction using artificial immune systems
ICARIS'07 Proceedings of the 6th international conference on Artificial immune systems
Using rough set and worst practice DEA in business failure prediction
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part II
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Bankruptcy prediction for credit risk using neural networks: A survey and new results
IEEE Transactions on Neural Networks
A hybrid device for the solution of sampling bias problems in the forecasting of firms' bankruptcy
Expert Systems with Applications: An International Journal
Optimal training subset in a support vector regression electric load forecasting model
Applied Soft Computing
Enhanced default risk models with SVM+
Expert Systems with Applications: An International Journal
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advances in computational intelligence - Volume Part I
International Journal of Mobile Learning and Organisation
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Bankruptcy prediction has been a topic of active research for business and corporate organizations since past few decades. The problem has been tackled using various models viz., Statistical, Market Based and Computational Intelligence in the past. Among Computational Intelligence models, Artificial Neural Network has become dominant modeling paradigm. In this Paper, we use a novel Soft Computing tool viz., Fuzzy Support Vector Machine (FSVM) to solve bankruptcy prediction problem. Support Vector Machine is a powerful statistical classification technique based on the idea of Structural Risk Minimization. Fuzzy Sets are capable of handling uncertainty and impreciseness in corporate data. Thus, using the advantage of Machine Learning and Fuzzy Sets prediction accuracy of whole model is enhanced. FSVM is implemented for analyzing predictors as financial ratios. A method of adapting it to default probability estimation is proposed. The test dataset comprises of 50 largest bankrupt organizations with capitalization of no less than $1 billion that filed for protection against creditors under Chapter 11 of United States Bankruptcy Code in 2001-2002 after stock marked crash of 2000. Experimental results on FSVM illustrate that it is better capable of extracting useful information from corporate data. This is followed by a comparative study of FSVM with other approaches. FSVM is effective in finding optimal feature subset and parameters. This is evident from the results thus improving prediction of bankruptcy. The choice of feature subset has positive influence on appropriate kernel parameters and vice versa which demonstrate its appreciable generalization performance than traditional bankruptcy prediction methods. Choosing appropriate value of parameter plays an important role on the performance of FSVM model. The effect of variability in prediction performance of FSVM with respect to various values of different parameters of SVM is also investigated. Finally, a comparative study of clustering power of FSVM is made with PNN on ripley and bankruptcy datasets. The results show that FSVM has superior clustering power than PNN.